Every year approximately 30,000 people in the United States and one million worldwide die as a result of suicide. Over the last 100 years, suicides have out-numbered homicides by at least 3:2. Concerned with high suicide rates, several federal agencies joined together and asked the Institute of Medicine to convene the Committee on Pathophysiology and Prevention of Adolescent and Adult Suicide to examine the state of the science base, gaps in our knowledge, strategies for prevention, and research designs for the study of suicide.

Reducing Suicide: A National Imperative

El Niño, La Niña, and the Southern Oscillation

Twenty-two monthly statistical quantities make up this dataset of grids which describes the atmospheric general circulation at eleven vertical levels across the troposphere and stratosphere over a 32-year period. These gridded analyses, created from upper air soundings across the globe, span the period from May 1958 to December 1989. The statistical parameters, which are useful for comparing climate model simulations to actual observations, include monthly means and variances of horizontal and vertical wind, temperature, geopotential height, moisture, and horizontal wind divergence. There are also covariance terms describing the relationship between wind, temperature, moisture, and geopotential height. In general, the analyses near the surface of the earth (900mb and below) tend to be less reliable than those of the higher levels because the individual rawinsonde reports are often incomplete due to missing 1000mb reports.

Global Atmospheric Circulation Statistics, 1958-1989

The prevalence of illicit-drug or alcohol use during pregnancy and discrepancies in mandatory reporting in Pinellas County, Florida

Smart manufacturing is strongly correlated with the digitization of all manufacturing activities. This increases the amount of data available to drive productivity and profit through data-driven decision making programs. The goal of this article is to assist data engineers in designing big data analysis pipelines for manufacturing process data. Thus, this paper characterizes the requirements for process data analysis pipelines and surveys existing platforms from academic literature. The results demonstrate a stronger focus on the storage and analysis phases of pipelines than on the ingestion, communication, and visualization stages. Results also show a tendency towards custom tools for ingestion and visualization, and relational data tools for storage and analysis. Tools for handling heterogeneous data are generally well-represented throughout the pipeline. Finally, batch processing tools are more widely adopted than real-time stream processing frameworks, and most pipelines opt for a common script-based data processing approach. Based on these results, recommendations are offered for each phase of the pipeline.

https://link.springer.com/content/pdf/10.1186%2Fs40537-018-0162-3.pdf

Manufacturing process data analysis pipelines: a requirements analysis and survey

Big Data Application Architecture Pattern Recipes provides an insight into heterogeneous infrastructures, databases, and visualization and analytics tools used for realizing the architectures of big data solutions. Its problem-solution approach helps in selecting the right architecture to solve the problem at hand. In the process of reading through these problems, you will learn harness the power of new big data opportunities which various enterprises use to attain real-time profits. Big Data Application Architecture Pattern Recipes answers one of the most critical questions of this time 'how do you select the best end-to-end architecture to solve your big data problem?'. The book deals with various mission critical problems encountered by solution architects, consultants, and software architects while dealing with the myriad options available for implementing a typical solution, trying to extract insight from huge volumes of data in realtime and across multiple relational and non-relational data types for clients from industries like retail, telecommunication, banking, and insurance. The patterns in this book providethe strong architectural foundation required to launch your next big data application. The architectures for realizing these opportunities are based on relatively less expensive and heterogeneous infrastructures compared to the traditional monolithic and hugely expensive options that exist currently. This book describes and evaluates the benefits of heterogeneity which brings with it multiple options of solving the same problem, evaluation of trade-offs and validation of 'fitness-for-purpose' of the solution. What youll learn Major considerations in building a big data solution Big data application architectures problems for specific industries What are the components one needs to build andend-to-end big data solution? Does one really need a real-time big data solution or an off-line analytics batch solution? What are the operations and support architectures fora big data solution? What are the scalability considerations, and options for a Hadoop installation? Who this book is for CIOs, CTOs, enterprise architects, and software architects Consultants, solution architects, and information management (IM) analysts who want to architect a big data solution for their enterprise

Big Data Application Architecture Q&A: A Problem - Solution Approach

Big Data Application Architecture Q&A provides an insight into heterogeneous infrastructures, databases, and visualization and analytics tools used for realizing the architectures of big data solutions. Its problem-solution approach helps in selecting the right architecture to solve the problem at hand. In the process of reading through these problems, you will learn harness the power of new big data opportunities which various enterprises use to attain real-time profits. Big Data Application Architecture Q&A answers one of the most critical questions of this time ‘how do you select the best end-to-end architecture to solve your big data problem?’. The book deals with various mission critical problems encountered by solution architects, consultants, and software architects while dealing with the myriad options available for implementing a typical solution, trying to extract insight from huge volumes of data in real—time and across multiple relational and non-relational data types for clients from industries like retail, telecommunication, banking, and insurance. The patterns in this book provide the strong architectural foundation required to launch your next big data application.

Big Data Application Architecture Q & A

Foodgrain supply, distribution, and consumption policies within a dual pricing mechanism: A case study of Bangladesh.

References and Bibliography

Enterprises and their customers have become very diverse and complex with the digitalization of business. Managing the information captured from these customers and markets to gain a competitive advantage has become a very expensive proposition when using the traditional data analytics methods, which are based on structured relational databases. This dilemma applies not only to businesses, but to research organizations, governments, and educational institutions that need less expensive computing and storage power to analyze complex scenarios and models involving images, video, and other data, as well as textual data.

Big Data Application Architecture

As you will see, this entire book is in problem-solution format. This chapter discusses topics in big data in a general sense, so it is not as technical as other chapters. The idea is to make sure you have a basic foundation for learning about big data. Other chapters will provide depth of coverage that we hope you will find useful no matter what your background. So let’s get started.

Nitin Sawant

Papers

Big Data Application Architecture Q&A: A Problem - Solution Approach

Big Data Application Architecture Q & A

References and Bibliography

Big Data Application Architecture

Big Data Introduction